Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Tuesday 14 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 175,857 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 175,847 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 1
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 0
## 133 2020-07-11 East of England 1
## 134 2020-07-12 East of England 0
## 135 2020-03-01 London 0
## 136 2020-03-02 London 0
## 137 2020-03-03 London 0
## 138 2020-03-04 London 0
## 139 2020-03-05 London 0
## 140 2020-03-06 London 1
## 141 2020-03-07 London 0
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## 148 2020-03-14 London 13
## 149 2020-03-15 London 9
## 150 2020-03-16 London 15
## 151 2020-03-17 London 23
## 152 2020-03-18 London 27
## 153 2020-03-19 London 25
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## 157 2020-03-23 London 63
## 158 2020-03-24 London 86
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## 162 2020-03-28 London 122
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## 218 2020-05-23 London 6
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## 220 2020-05-25 London 9
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## 270 2020-03-02 Midlands 0
## 271 2020-03-03 Midlands 1
## 272 2020-03-04 Midlands 0
## 273 2020-03-05 Midlands 0
## 274 2020-03-06 Midlands 0
## 275 2020-03-07 Midlands 0
## 276 2020-03-08 Midlands 2
## 277 2020-03-09 Midlands 1
## 278 2020-03-10 Midlands 0
## 279 2020-03-11 Midlands 2
## 280 2020-03-12 Midlands 6
## 281 2020-03-13 Midlands 5
## 282 2020-03-14 Midlands 4
## 283 2020-03-15 Midlands 5
## 284 2020-03-16 Midlands 11
## 285 2020-03-17 Midlands 8
## 286 2020-03-18 Midlands 13
## 287 2020-03-19 Midlands 8
## 288 2020-03-20 Midlands 28
## 289 2020-03-21 Midlands 13
## 290 2020-03-22 Midlands 31
## 291 2020-03-23 Midlands 33
## 292 2020-03-24 Midlands 41
## 293 2020-03-25 Midlands 48
## 294 2020-03-26 Midlands 64
## 295 2020-03-27 Midlands 72
## 296 2020-03-28 Midlands 89
## 297 2020-03-29 Midlands 92
## 298 2020-03-30 Midlands 90
## 299 2020-03-31 Midlands 123
## 300 2020-04-01 Midlands 140
## 301 2020-04-02 Midlands 142
## 302 2020-04-03 Midlands 124
## 303 2020-04-04 Midlands 151
## 304 2020-04-05 Midlands 164
## 305 2020-04-06 Midlands 140
## 306 2020-04-07 Midlands 123
## 307 2020-04-08 Midlands 186
## 308 2020-04-09 Midlands 139
## 309 2020-04-10 Midlands 127
## 310 2020-04-11 Midlands 142
## 311 2020-04-12 Midlands 139
## 312 2020-04-13 Midlands 120
## 313 2020-04-14 Midlands 116
## 314 2020-04-15 Midlands 147
## 315 2020-04-16 Midlands 102
## 316 2020-04-17 Midlands 118
## 317 2020-04-18 Midlands 115
## 318 2020-04-19 Midlands 92
## 319 2020-04-20 Midlands 107
## 320 2020-04-21 Midlands 86
## 321 2020-04-22 Midlands 78
## 322 2020-04-23 Midlands 103
## 323 2020-04-24 Midlands 79
## 324 2020-04-25 Midlands 72
## 325 2020-04-26 Midlands 81
## 326 2020-04-27 Midlands 74
## 327 2020-04-28 Midlands 68
## 328 2020-04-29 Midlands 53
## 329 2020-04-30 Midlands 56
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## 331 2020-05-02 Midlands 51
## 332 2020-05-03 Midlands 52
## 333 2020-05-04 Midlands 61
## 334 2020-05-05 Midlands 59
## 335 2020-05-06 Midlands 59
## 336 2020-05-07 Midlands 48
## 337 2020-05-08 Midlands 34
## 338 2020-05-09 Midlands 37
## 339 2020-05-10 Midlands 42
## 340 2020-05-11 Midlands 33
## 341 2020-05-12 Midlands 45
## 342 2020-05-13 Midlands 40
## 343 2020-05-14 Midlands 37
## 344 2020-05-15 Midlands 40
## 345 2020-05-16 Midlands 34
## 346 2020-05-17 Midlands 31
## 347 2020-05-18 Midlands 35
## 348 2020-05-19 Midlands 34
## 349 2020-05-20 Midlands 36
## 350 2020-05-21 Midlands 32
## 351 2020-05-22 Midlands 27
## 352 2020-05-23 Midlands 34
## 353 2020-05-24 Midlands 20
## 354 2020-05-25 Midlands 26
## 355 2020-05-26 Midlands 33
## 356 2020-05-27 Midlands 29
## 357 2020-05-28 Midlands 28
## 358 2020-05-29 Midlands 20
## 359 2020-05-30 Midlands 21
## 360 2020-05-31 Midlands 22
## 361 2020-06-01 Midlands 20
## 362 2020-06-02 Midlands 22
## 363 2020-06-03 Midlands 24
## 364 2020-06-04 Midlands 16
## 365 2020-06-05 Midlands 21
## 366 2020-06-06 Midlands 20
## 367 2020-06-07 Midlands 17
## 368 2020-06-08 Midlands 16
## 369 2020-06-09 Midlands 18
## 370 2020-06-10 Midlands 15
## 371 2020-06-11 Midlands 13
## 372 2020-06-12 Midlands 12
## 373 2020-06-13 Midlands 6
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## 375 2020-06-15 Midlands 12
## 376 2020-06-16 Midlands 15
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## 385 2020-06-25 Midlands 18
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## 399 2020-07-09 Midlands 6
## 400 2020-07-10 Midlands 2
## 401 2020-07-11 Midlands 0
## 402 2020-07-12 Midlands 1
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## 405 2020-03-03 North East and Yorkshire 0
## 406 2020-03-04 North East and Yorkshire 0
## 407 2020-03-05 North East and Yorkshire 0
## 408 2020-03-06 North East and Yorkshire 0
## 409 2020-03-07 North East and Yorkshire 0
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## 412 2020-03-10 North East and Yorkshire 0
## 413 2020-03-11 North East and Yorkshire 0
## 414 2020-03-12 North East and Yorkshire 0
## 415 2020-03-13 North East and Yorkshire 0
## 416 2020-03-14 North East and Yorkshire 0
## 417 2020-03-15 North East and Yorkshire 2
## 418 2020-03-16 North East and Yorkshire 3
## 419 2020-03-17 North East and Yorkshire 1
## 420 2020-03-18 North East and Yorkshire 2
## 421 2020-03-19 North East and Yorkshire 6
## 422 2020-03-20 North East and Yorkshire 5
## 423 2020-03-21 North East and Yorkshire 6
## 424 2020-03-22 North East and Yorkshire 7
## 425 2020-03-23 North East and Yorkshire 9
## 426 2020-03-24 North East and Yorkshire 8
## 427 2020-03-25 North East and Yorkshire 18
## 428 2020-03-26 North East and Yorkshire 21
## 429 2020-03-27 North East and Yorkshire 28
## 430 2020-03-28 North East and Yorkshire 35
## 431 2020-03-29 North East and Yorkshire 38
## 432 2020-03-30 North East and Yorkshire 64
## 433 2020-03-31 North East and Yorkshire 60
## 434 2020-04-01 North East and Yorkshire 67
## 435 2020-04-02 North East and Yorkshire 75
## 436 2020-04-03 North East and Yorkshire 100
## 437 2020-04-04 North East and Yorkshire 105
## 438 2020-04-05 North East and Yorkshire 92
## 439 2020-04-06 North East and Yorkshire 96
## 440 2020-04-07 North East and Yorkshire 102
## 441 2020-04-08 North East and Yorkshire 107
## 442 2020-04-09 North East and Yorkshire 111
## 443 2020-04-10 North East and Yorkshire 117
## 444 2020-04-11 North East and Yorkshire 98
## 445 2020-04-12 North East and Yorkshire 84
## 446 2020-04-13 North East and Yorkshire 94
## 447 2020-04-14 North East and Yorkshire 107
## 448 2020-04-15 North East and Yorkshire 96
## 449 2020-04-16 North East and Yorkshire 103
## 450 2020-04-17 North East and Yorkshire 88
## 451 2020-04-18 North East and Yorkshire 95
## 452 2020-04-19 North East and Yorkshire 88
## 453 2020-04-20 North East and Yorkshire 100
## 454 2020-04-21 North East and Yorkshire 76
## 455 2020-04-22 North East and Yorkshire 84
## 456 2020-04-23 North East and Yorkshire 63
## 457 2020-04-24 North East and Yorkshire 72
## 458 2020-04-25 North East and Yorkshire 69
## 459 2020-04-26 North East and Yorkshire 65
## 460 2020-04-27 North East and Yorkshire 65
## 461 2020-04-28 North East and Yorkshire 57
## 462 2020-04-29 North East and Yorkshire 69
## 463 2020-04-30 North East and Yorkshire 57
## 464 2020-05-01 North East and Yorkshire 64
## 465 2020-05-02 North East and Yorkshire 48
## 466 2020-05-03 North East and Yorkshire 40
## 467 2020-05-04 North East and Yorkshire 49
## 468 2020-05-05 North East and Yorkshire 40
## 469 2020-05-06 North East and Yorkshire 51
## 470 2020-05-07 North East and Yorkshire 45
## 471 2020-05-08 North East and Yorkshire 42
## 472 2020-05-09 North East and Yorkshire 44
## 473 2020-05-10 North East and Yorkshire 40
## 474 2020-05-11 North East and Yorkshire 29
## 475 2020-05-12 North East and Yorkshire 27
## 476 2020-05-13 North East and Yorkshire 28
## 477 2020-05-14 North East and Yorkshire 31
## 478 2020-05-15 North East and Yorkshire 32
## 479 2020-05-16 North East and Yorkshire 35
## 480 2020-05-17 North East and Yorkshire 26
## 481 2020-05-18 North East and Yorkshire 30
## 482 2020-05-19 North East and Yorkshire 27
## 483 2020-05-20 North East and Yorkshire 22
## 484 2020-05-21 North East and Yorkshire 33
## 485 2020-05-22 North East and Yorkshire 22
## 486 2020-05-23 North East and Yorkshire 18
## 487 2020-05-24 North East and Yorkshire 26
## 488 2020-05-25 North East and Yorkshire 21
## 489 2020-05-26 North East and Yorkshire 21
## 490 2020-05-27 North East and Yorkshire 22
## 491 2020-05-28 North East and Yorkshire 21
## 492 2020-05-29 North East and Yorkshire 25
## 493 2020-05-30 North East and Yorkshire 20
## 494 2020-05-31 North East and Yorkshire 20
## 495 2020-06-01 North East and Yorkshire 17
## 496 2020-06-02 North East and Yorkshire 23
## 497 2020-06-03 North East and Yorkshire 23
## 498 2020-06-04 North East and Yorkshire 17
## 499 2020-06-05 North East and Yorkshire 18
## 500 2020-06-06 North East and Yorkshire 21
## 501 2020-06-07 North East and Yorkshire 14
## 502 2020-06-08 North East and Yorkshire 11
## 503 2020-06-09 North East and Yorkshire 12
## 504 2020-06-10 North East and Yorkshire 19
## 505 2020-06-11 North East and Yorkshire 7
## 506 2020-06-12 North East and Yorkshire 9
## 507 2020-06-13 North East and Yorkshire 10
## 508 2020-06-14 North East and Yorkshire 11
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## 524 2020-06-30 North East and Yorkshire 4
## 525 2020-07-01 North East and Yorkshire 1
## 526 2020-07-02 North East and Yorkshire 4
## 527 2020-07-03 North East and Yorkshire 3
## 528 2020-07-04 North East and Yorkshire 4
## 529 2020-07-05 North East and Yorkshire 2
## 530 2020-07-06 North East and Yorkshire 2
## 531 2020-07-07 North East and Yorkshire 3
## 532 2020-07-08 North East and Yorkshire 3
## 533 2020-07-09 North East and Yorkshire 0
## 534 2020-07-10 North East and Yorkshire 2
## 535 2020-07-11 North East and Yorkshire 1
## 536 2020-07-12 North East and Yorkshire 0
## 537 2020-03-01 North West 0
## 538 2020-03-02 North West 0
## 539 2020-03-03 North West 0
## 540 2020-03-04 North West 0
## 541 2020-03-05 North West 1
## 542 2020-03-06 North West 0
## 543 2020-03-07 North West 0
## 544 2020-03-08 North West 1
## 545 2020-03-09 North West 0
## 546 2020-03-10 North West 0
## 547 2020-03-11 North West 0
## 548 2020-03-12 North West 2
## 549 2020-03-13 North West 3
## 550 2020-03-14 North West 1
## 551 2020-03-15 North West 4
## 552 2020-03-16 North West 2
## 553 2020-03-17 North West 4
## 554 2020-03-18 North West 6
## 555 2020-03-19 North West 7
## 556 2020-03-20 North West 10
## 557 2020-03-21 North West 11
## 558 2020-03-22 North West 13
## 559 2020-03-23 North West 15
## 560 2020-03-24 North West 21
## 561 2020-03-25 North West 21
## 562 2020-03-26 North West 29
## 563 2020-03-27 North West 36
## 564 2020-03-28 North West 28
## 565 2020-03-29 North West 46
## 566 2020-03-30 North West 67
## 567 2020-03-31 North West 52
## 568 2020-04-01 North West 86
## 569 2020-04-02 North West 96
## 570 2020-04-03 North West 95
## 571 2020-04-04 North West 98
## 572 2020-04-05 North West 102
## 573 2020-04-06 North West 100
## 574 2020-04-07 North West 135
## 575 2020-04-08 North West 127
## 576 2020-04-09 North West 119
## 577 2020-04-10 North West 117
## 578 2020-04-11 North West 138
## 579 2020-04-12 North West 125
## 580 2020-04-13 North West 129
## 581 2020-04-14 North West 131
## 582 2020-04-15 North West 114
## 583 2020-04-16 North West 135
## 584 2020-04-17 North West 98
## 585 2020-04-18 North West 113
## 586 2020-04-19 North West 71
## 587 2020-04-20 North West 83
## 588 2020-04-21 North West 76
## 589 2020-04-22 North West 86
## 590 2020-04-23 North West 85
## 591 2020-04-24 North West 66
## 592 2020-04-25 North West 66
## 593 2020-04-26 North West 55
## 594 2020-04-27 North West 54
## 595 2020-04-28 North West 57
## 596 2020-04-29 North West 63
## 597 2020-04-30 North West 59
## 598 2020-05-01 North West 45
## 599 2020-05-02 North West 56
## 600 2020-05-03 North West 55
## 601 2020-05-04 North West 48
## 602 2020-05-05 North West 48
## 603 2020-05-06 North West 44
## 604 2020-05-07 North West 49
## 605 2020-05-08 North West 42
## 606 2020-05-09 North West 30
## 607 2020-05-10 North West 42
## 608 2020-05-11 North West 35
## 609 2020-05-12 North West 38
## 610 2020-05-13 North West 25
## 611 2020-05-14 North West 26
## 612 2020-05-15 North West 33
## 613 2020-05-16 North West 32
## 614 2020-05-17 North West 24
## 615 2020-05-18 North West 31
## 616 2020-05-19 North West 35
## 617 2020-05-20 North West 27
## 618 2020-05-21 North West 27
## 619 2020-05-22 North West 26
## 620 2020-05-23 North West 31
## 621 2020-05-24 North West 26
## 622 2020-05-25 North West 31
## 623 2020-05-26 North West 27
## 624 2020-05-27 North West 27
## 625 2020-05-28 North West 28
## 626 2020-05-29 North West 20
## 627 2020-05-30 North West 19
## 628 2020-05-31 North West 13
## 629 2020-06-01 North West 12
## 630 2020-06-02 North West 27
## 631 2020-06-03 North West 22
## 632 2020-06-04 North West 22
## 633 2020-06-05 North West 16
## 634 2020-06-06 North West 26
## 635 2020-06-07 North West 20
## 636 2020-06-08 North West 23
## 637 2020-06-09 North West 17
## 638 2020-06-10 North West 16
## 639 2020-06-11 North West 16
## 640 2020-06-12 North West 11
## 641 2020-06-13 North West 10
## 642 2020-06-14 North West 15
## 643 2020-06-15 North West 16
## 644 2020-06-16 North West 15
## 645 2020-06-17 North West 13
## 646 2020-06-18 North West 13
## 647 2020-06-19 North West 7
## 648 2020-06-20 North West 11
## 649 2020-06-21 North West 7
## 650 2020-06-22 North West 11
## 651 2020-06-23 North West 13
## 652 2020-06-24 North West 13
## 653 2020-06-25 North West 15
## 654 2020-06-26 North West 6
## 655 2020-06-27 North West 7
## 656 2020-06-28 North West 9
## 657 2020-06-29 North West 7
## 658 2020-06-30 North West 6
## 659 2020-07-01 North West 3
## 660 2020-07-02 North West 6
## 661 2020-07-03 North West 6
## 662 2020-07-04 North West 4
## 663 2020-07-05 North West 6
## 664 2020-07-06 North West 9
## 665 2020-07-07 North West 7
## 666 2020-07-08 North West 4
## 667 2020-07-09 North West 9
## 668 2020-07-10 North West 1
## 669 2020-07-11 North West 1
## 670 2020-07-12 North West 0
## 671 2020-03-01 South East 0
## 672 2020-03-02 South East 0
## 673 2020-03-03 South East 1
## 674 2020-03-04 South East 0
## 675 2020-03-05 South East 1
## 676 2020-03-06 South East 0
## 677 2020-03-07 South East 0
## 678 2020-03-08 South East 1
## 679 2020-03-09 South East 1
## 680 2020-03-10 South East 1
## 681 2020-03-11 South East 1
## 682 2020-03-12 South East 0
## 683 2020-03-13 South East 1
## 684 2020-03-14 South East 1
## 685 2020-03-15 South East 5
## 686 2020-03-16 South East 8
## 687 2020-03-17 South East 7
## 688 2020-03-18 South East 10
## 689 2020-03-19 South East 9
## 690 2020-03-20 South East 13
## 691 2020-03-21 South East 7
## 692 2020-03-22 South East 25
## 693 2020-03-23 South East 20
## 694 2020-03-24 South East 22
## 695 2020-03-25 South East 29
## 696 2020-03-26 South East 35
## 697 2020-03-27 South East 34
## 698 2020-03-28 South East 36
## 699 2020-03-29 South East 55
## 700 2020-03-30 South East 58
## 701 2020-03-31 South East 65
## 702 2020-04-01 South East 66
## 703 2020-04-02 South East 55
## 704 2020-04-03 South East 72
## 705 2020-04-04 South East 80
## 706 2020-04-05 South East 82
## 707 2020-04-06 South East 88
## 708 2020-04-07 South East 100
## 709 2020-04-08 South East 83
## 710 2020-04-09 South East 104
## 711 2020-04-10 South East 88
## 712 2020-04-11 South East 88
## 713 2020-04-12 South East 88
## 714 2020-04-13 South East 84
## 715 2020-04-14 South East 65
## 716 2020-04-15 South East 72
## 717 2020-04-16 South East 56
## 718 2020-04-17 South East 86
## 719 2020-04-18 South East 57
## 720 2020-04-19 South East 70
## 721 2020-04-20 South East 87
## 722 2020-04-21 South East 51
## 723 2020-04-22 South East 54
## 724 2020-04-23 South East 57
## 725 2020-04-24 South East 64
## 726 2020-04-25 South East 51
## 727 2020-04-26 South East 51
## 728 2020-04-27 South East 40
## 729 2020-04-28 South East 40
## 730 2020-04-29 South East 47
## 731 2020-04-30 South East 29
## 732 2020-05-01 South East 37
## 733 2020-05-02 South East 36
## 734 2020-05-03 South East 17
## 735 2020-05-04 South East 35
## 736 2020-05-05 South East 29
## 737 2020-05-06 South East 25
## 738 2020-05-07 South East 27
## 739 2020-05-08 South East 26
## 740 2020-05-09 South East 28
## 741 2020-05-10 South East 19
## 742 2020-05-11 South East 25
## 743 2020-05-12 South East 27
## 744 2020-05-13 South East 18
## 745 2020-05-14 South East 32
## 746 2020-05-15 South East 25
## 747 2020-05-16 South East 22
## 748 2020-05-17 South East 18
## 749 2020-05-18 South East 22
## 750 2020-05-19 South East 12
## 751 2020-05-20 South East 22
## 752 2020-05-21 South East 15
## 753 2020-05-22 South East 17
## 754 2020-05-23 South East 21
## 755 2020-05-24 South East 17
## 756 2020-05-25 South East 13
## 757 2020-05-26 South East 19
## 758 2020-05-27 South East 18
## 759 2020-05-28 South East 12
## 760 2020-05-29 South East 21
## 761 2020-05-30 South East 8
## 762 2020-05-31 South East 12
## 763 2020-06-01 South East 11
## 764 2020-06-02 South East 13
## 765 2020-06-03 South East 18
## 766 2020-06-04 South East 11
## 767 2020-06-05 South East 11
## 768 2020-06-06 South East 10
## 769 2020-06-07 South East 12
## 770 2020-06-08 South East 8
## 771 2020-06-09 South East 10
## 772 2020-06-10 South East 11
## 773 2020-06-11 South East 5
## 774 2020-06-12 South East 6
## 775 2020-06-13 South East 6
## 776 2020-06-14 South East 7
## 777 2020-06-15 South East 8
## 778 2020-06-16 South East 13
## 779 2020-06-17 South East 9
## 780 2020-06-18 South East 4
## 781 2020-06-19 South East 7
## 782 2020-06-20 South East 5
## 783 2020-06-21 South East 3
## 784 2020-06-22 South East 2
## 785 2020-06-23 South East 8
## 786 2020-06-24 South East 7
## 787 2020-06-25 South East 5
## 788 2020-06-26 South East 8
## 789 2020-06-27 South East 8
## 790 2020-06-28 South East 6
## 791 2020-06-29 South East 5
## 792 2020-06-30 South East 5
## 793 2020-07-01 South East 2
## 794 2020-07-02 South East 7
## 795 2020-07-03 South East 3
## 796 2020-07-04 South East 5
## 797 2020-07-05 South East 4
## 798 2020-07-06 South East 3
## 799 2020-07-07 South East 5
## 800 2020-07-08 South East 3
## 801 2020-07-09 South East 7
## 802 2020-07-10 South East 3
## 803 2020-07-11 South East 0
## 804 2020-07-12 South East 0
## 805 2020-03-01 South West 0
## 806 2020-03-02 South West 0
## 807 2020-03-03 South West 0
## 808 2020-03-04 South West 0
## 809 2020-03-05 South West 0
## 810 2020-03-06 South West 0
## 811 2020-03-07 South West 0
## 812 2020-03-08 South West 0
## 813 2020-03-09 South West 0
## 814 2020-03-10 South West 0
## 815 2020-03-11 South West 1
## 816 2020-03-12 South West 0
## 817 2020-03-13 South West 0
## 818 2020-03-14 South West 1
## 819 2020-03-15 South West 0
## 820 2020-03-16 South West 0
## 821 2020-03-17 South West 2
## 822 2020-03-18 South West 2
## 823 2020-03-19 South West 4
## 824 2020-03-20 South West 3
## 825 2020-03-21 South West 6
## 826 2020-03-22 South West 7
## 827 2020-03-23 South West 8
## 828 2020-03-24 South West 7
## 829 2020-03-25 South West 9
## 830 2020-03-26 South West 11
## 831 2020-03-27 South West 13
## 832 2020-03-28 South West 21
## 833 2020-03-29 South West 18
## 834 2020-03-30 South West 23
## 835 2020-03-31 South West 23
## 836 2020-04-01 South West 21
## 837 2020-04-02 South West 23
## 838 2020-04-03 South West 30
## 839 2020-04-04 South West 42
## 840 2020-04-05 South West 32
## 841 2020-04-06 South West 34
## 842 2020-04-07 South West 39
## 843 2020-04-08 South West 47
## 844 2020-04-09 South West 24
## 845 2020-04-10 South West 46
## 846 2020-04-11 South West 43
## 847 2020-04-12 South West 23
## 848 2020-04-13 South West 27
## 849 2020-04-14 South West 24
## 850 2020-04-15 South West 32
## 851 2020-04-16 South West 29
## 852 2020-04-17 South West 33
## 853 2020-04-18 South West 25
## 854 2020-04-19 South West 31
## 855 2020-04-20 South West 26
## 856 2020-04-21 South West 26
## 857 2020-04-22 South West 23
## 858 2020-04-23 South West 17
## 859 2020-04-24 South West 19
## 860 2020-04-25 South West 15
## 861 2020-04-26 South West 27
## 862 2020-04-27 South West 13
## 863 2020-04-28 South West 17
## 864 2020-04-29 South West 15
## 865 2020-04-30 South West 26
## 866 2020-05-01 South West 6
## 867 2020-05-02 South West 7
## 868 2020-05-03 South West 10
## 869 2020-05-04 South West 17
## 870 2020-05-05 South West 14
## 871 2020-05-06 South West 19
## 872 2020-05-07 South West 16
## 873 2020-05-08 South West 6
## 874 2020-05-09 South West 11
## 875 2020-05-10 South West 5
## 876 2020-05-11 South West 8
## 877 2020-05-12 South West 7
## 878 2020-05-13 South West 7
## 879 2020-05-14 South West 6
## 880 2020-05-15 South West 4
## 881 2020-05-16 South West 4
## 882 2020-05-17 South West 6
## 883 2020-05-18 South West 4
## 884 2020-05-19 South West 6
## 885 2020-05-20 South West 1
## 886 2020-05-21 South West 9
## 887 2020-05-22 South West 6
## 888 2020-05-23 South West 6
## 889 2020-05-24 South West 3
## 890 2020-05-25 South West 8
## 891 2020-05-26 South West 11
## 892 2020-05-27 South West 5
## 893 2020-05-28 South West 10
## 894 2020-05-29 South West 7
## 895 2020-05-30 South West 3
## 896 2020-05-31 South West 2
## 897 2020-06-01 South West 7
## 898 2020-06-02 South West 2
## 899 2020-06-03 South West 7
## 900 2020-06-04 South West 2
## 901 2020-06-05 South West 2
## 902 2020-06-06 South West 1
## 903 2020-06-07 South West 3
## 904 2020-06-08 South West 3
## 905 2020-06-09 South West 0
## 906 2020-06-10 South West 1
## 907 2020-06-11 South West 2
## 908 2020-06-12 South West 2
## 909 2020-06-13 South West 2
## 910 2020-06-14 South West 0
## 911 2020-06-15 South West 1
## 912 2020-06-16 South West 2
## 913 2020-06-17 South West 0
## 914 2020-06-18 South West 0
## 915 2020-06-19 South West 0
## 916 2020-06-20 South West 2
## 917 2020-06-21 South West 0
## 918 2020-06-22 South West 1
## 919 2020-06-23 South West 1
## 920 2020-06-24 South West 1
## 921 2020-06-25 South West 0
## 922 2020-06-26 South West 3
## 923 2020-06-27 South West 0
## 924 2020-06-28 South West 0
## 925 2020-06-29 South West 1
## 926 2020-06-30 South West 0
## 927 2020-07-01 South West 0
## 928 2020-07-02 South West 0
## 929 2020-07-03 South West 0
## 930 2020-07-04 South West 0
## 931 2020-07-05 South West 1
## 932 2020-07-06 South West 0
## 933 2020-07-07 South West 0
## 934 2020-07-08 South West 2
## 935 2020-07-09 South West 0
## 936 2020-07-10 South West 1
## 937 2020-07-11 South West 0
## 938 2020-07-12 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 13 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -12.8382 -4.1709 -0.1345 3.4857 7.8894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.635e+00 6.380e-02 72.64 <2e-16 ***
## note_lag 1.421e-05 6.618e-07 21.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 22.6606)
##
## Null deviance: 11338.8 on 72 degrees of freedom
## Residual deviance: 1710.1 on 71 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 103.005729 1.000014
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 90.723906 116.509530
## note_lag 1.000013 1.000016
Rsq(lag_mod)
## [1] 0.8491787
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.1
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.1 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1